Rapid material development process for additive manufactured materials

ABSTRACT

A rapid material development process for a powder bed fusion additive manufacturing (PBF AM) process generally utilizes a computational fluid dynamics (CFD) simulation to facilitate selection of a simulated parameter set, which can then be used in a design of experiments (DOE) to generate an orthogonal parameter space to predict an ideal parameter set. The orthogonal parameter space defined by the DOE can then be used to generate a multitude of reduced volume build samples using PBF AM with varying laser or electron beam parameters and/or feedstock chemistries. The reduced volume build samples are mechanically characterized using high throughput techniques and analyzed to provide an optimal parameter set for a 3D article or a validation sample, which provides an increased understanding of the parameters and their independent and confounding effects on defects and microstructure. Additionally, machine learning techniques can be used to optimize for future parameter selection by modeling the relationship between input processing parameters and outputs of material characterization.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application Ser.No. 63/028,131, filed on May 21, 2020, which is expressly incorporatedby reference herein in its entirety.

BACKGROUND

The present disclosure generally relates to rapid material developmentprocesses for additive manufactured materials. More particularly, therapid material development processes utilize simulation and intelligentdesign following the simulation to build and analyze multiple reducedvolume samples from different feedstock chemistries and/or differentlaser parameters. Physical outputs of the multiple reduced volumesamples are quantified by mechanical characterization to allow for rapidoptimization and adjustment of the process control parameters so as toselect a desired quality and provide a robust processing space. In thismanner, one can predict material formation with respect to defects ormicrostructure and the relevant mechanical performance associated withthe material synthesis.

Additive manufacturing (AM) processes are fabrication techniques thatallow one to produce functional complex parts layer-by-layer, withoutthe use of molds or dies. AM processes allow for fabrication of complexdesign features to be incorporated. There are a variety of methods ofadditive manufacturing utilizing a variety of different feedstockmaterials, e.g., plastics, metals, ceramics, composites, or the like.For example, powder bed fusion (PBF) is a subset of additivemanufacturing (AM), wherein a thermal energy source such as thatgenerated by a laser (L-PBF) or an electron beam (E-PBF) or directedenergy deposition (DED) is used to consolidate material in powder formto form three-dimensional (3D) articles. The thermal energy source isapplied to particles contained within a powder bed to melt, sinter orfuse the particles together. The powder bed is subsequently indexed downas each layer is completed to allow new powder to be spread over thebuild area, and for the layer-by-laver consolidation process to becontinued.

A typical PBF AM process begins with creation of a digital model that isconverted into a computer file, or computer aided design (“CAD”) data,defining the three dimensional article in two-dimensional layers, whichtypically range in thickness from about 20 micrometers to about 100micrometers. The CAD data can include geometric data relating to a size,shape, thickness, material, mass, or density of the article, as well asinternal features, passages, and structures. Next, a layer of powdermaterial is deposited on a work platform. A heat source, such as a laseror an electron beam, then selectively melts, sinters, or fuses themetallic powder over the platform. Once cooled, the melted, sintered orfused pattern becomes the first layer that is used to define thearticle. After the first layer is formed, the platform, along with thetwo-dimensional pattern in the first layer, lowers and un-fused powderfills in the void over the first layer. That powder is then melted,sintered, or fused to form a second layer. The process of building thearticle a single layer at a time is repeated until the complete 3Darticle is manufactured. Powder bed fusion methods work well with metalsas well as plastics, polymers, composites and ceramics.

As noted above, PBF AM is a heat driven process, which needs to bemodeled accurately. The large temperature gradients exhibited by PBF AMprocesses, for example, justify using temperature-dependent propertiessuch as absorbance, thermal diffusivity, surface tension and vaporpressure, which can strongly impact the final solidified structureduring modeling.

BRIEF SUMMARY

Disclosed herein are systems, computer implemented powder bed fusionrapid material development processes for additive manufacturedmaterials, and non-transitory computer readable mediums that whenexecuted by a processor, causes the processor to execute operations forparameter optimization of a powder fusion bed additive manufacturingprocess.

In one or more embodiments, the systems include at least one computercommunicatively coupled to a three-dimensional additive manufacturingprinter; and a mechanical characterization device configured to measureone or more physical outputs associated with additive manufacturedreduced volume samples. The additive manufactured reduced volume samplesare a fraction of an intended build article, and the at least onecomputer is configured to provide a computational fluid dynamicsimulation of a powder bed fusion additive manufacturing process andprovide a simulated optimal parameter set. The at least one computer isfurther configured to provide a first statistical design of experimentsbased on the simulated optimal parameter set to generate amulti-factorial parameter space encompassing the simulated opticalparameter set and provide instructions to the three-dimensional additivemanufacturing printer to build the reduced volume samples according tothe multi-factorial parameter space to determine a first optimal buildparameter set. The at least one computer is optionally configured toprovide at least one additional statistical design of experiments basedon the first optimal build parameter set to generate at least oneadditional multi-factorial parameter space encompassing the firstoptimal build parameter set and provide instructions to thethree-dimensional additive manufacturing printer to build the reducedvolume samples according to the at least one additional multi-factorialparameter space to determine at least one additional optimal buildparameter set from the first optimal build parameter set.

In one or more embodiments, a computer implemented powder bed fusionrapid material development process for additive manufactured materials,the computer implemented process includes modeling melt poolsolidification of the powder bed fusion additive manufactured materialsto produce a simulated parameter set; designing a multi-factorialparameter space encompassing the simulated parameter set; buildingmultiple additive manufactured samples for each parameter set within themulti-factorial parameter space, wherein the parameter set comprisesindependent parameters comprising layer thickness, hatch spacing,exposure time, scan velocity, power or combinations thereof, and whereinthe samples are at a reduced volume relative to an intended buildarticle; mechanically characterizing one or more physical outputs foreach of the samples built according to each parameter set; andcorrelating defect morphology associated with the one or more ofphysical outputs to one or more independent parameters within themulti-factorial parameter space used in building the multiple additivemanufactured samples to provide an optimal parameter set.

In one or more embodiments, a non-transitory computer readable mediumembodying computer-executable instructions, that when executed by aprocessor, causes the processor to execute operations for parameteroptimization of a powder fusion bed additive manufacturing processincludes modeling melt pool solidification for a feedstock compositionto produce a simulated parameter set; executing a multi-factorial designof experiments to define a parameter space encompassing the simulatedparameter set; providing instructions to a three dimensional additivemanufacturing printer to build multiple samples associated with eachparameter set within the parameter space, wherein each parameter setcomprises one or more of a layer thickness, a hatch spacing, an exposuretime, a scan velocity, a power or combinations thereof, and wherein thesamples are at a reduced volume relative to an intended build article;mechanically characterizing one or more physical outputs for each of themultiple additive manufactured samples; and correlating defectmorphology associated with the one or more physical outputs to one ormore of the parameters used in building the multiple additivemanufactured samples to provide an optimized parameter set.

The disclosure may be understood more readily by reference to thefollowing detailed description of the various features of the disclosureand the examples included therein.

BRIEF DESCRIPTION OF THE DRAWINGS

Referring now to the figures wherein the like elements are numberedalike:

FIG. 1A-C depicts an exemplary process flow for rapidly down selectingAM machine parameters to improve processing space in accordance with thepresent disclosure;

FIG. 2 pictorially and graphically illustrates simulation of anexemplary melt pool solidification depicting the formation of a lack offusion defect (top left), an ideal melt pool solidification having fulldensity (center left), a keyhole defect (bottom left), and a resultinglaser power-velocity processing space (right) generated from thesimulation data in accordance with the present disclosure;

FIG. 3 graphically illustrates porosity as a function of desirability ina statistical analysis of various additive manufacturing processparameters and desirability parameter validation graphicallyillustrating porosity as a function of energy density in accordance withthe present disclosure;

FIG. 4 graphically illustrates porosity as a function of energy densityby laser power and by exposure time for samples made with a laser powderbed fusion additive manufacturing process in accordance with the presentdisclosure;

FIG. 5 schematically illustrates a design of experiment sample set basedon exposure time and laser power for a laser powder bed fusion additivemanufacturing process in accordance with the present disclosure;

FIG. 6 graphically illustrates energy density for the design ofexperiment sample set of FIG. 5 in accordance with the presentdisclosure;

FIG. 7 graphically illustrates total porosity and deviation from sampleform for the design of experiment sample set of FIG. 5 in accordancewith the present disclosure

FIG. 8 pictorially illustrates sectional views and 3D renderings of theporosity through a cylindrical volume for selected samples in the designof experiment sample set of FIG. 5 in accordance with the presentdisclosure;

FIG. 9 graphically illustrates exposure time as a function of power foran additive manufacturing model depicting a predicted processing spaceincluding a modeled optimal initial setpoint in accordance with thepresent disclosure;

FIG. 10 graphically illustrates exposure time as a function of power foran additive manufacturing model subsequent to obtaining secondaryinformation from a build cycle based on the modeled optimal initialsetpoint of FIG. 9 in accordance with the present disclosure;

FIG. 11 graphically illustrates build samples from a multifactorialdesign for energy density based on a modeled optimal initial setpoint inaccordance with the present disclosure;

FIG. 12 graphically illustrates total porosity and surface roughness forthe build samples in accordance with the present disclosure;

FIG. 13 graphically illustrates total porosity as a function of energydensity for a type 316L steel feedstock chemistry with and without aceramic additive manufactured with a laser powder bed fusion additivemanufacturing process in accordance with the present disclosure; and

FIG. 14 illustrates an exemplary computer system for the rapiddevelopment additive manufacturing method of in accordance with one ormore embodiments of the present disclosure.

DETAILED DESCRIPTION

Disclosed herein are processes for rapidly developing materials forpowder bed fusion (PBF) additive manufacturing (AM). PBF AM utilizes aheat source to melt, sinter and/or fuse particles within a powder bed,wherein typically the heat source is a laser (L-PBF) or an electron beam(E-PBF). The rapid development processes for PBF AM described hereingenerally utilize a computer simulation model such as computationalfluid dynamics (CFD) simulation to facilitate selection of a simulatedparameter set, which can then be used in a design of experiments (DOE)to generate an orthogonal parameter space about the simulated parameterset to predict an optimized parameter set based on sample analysis.Additionally, machine learning techniques can be used to optimize forfuture parameter selection by modeling the relationship between inputprocessing parameters and outputs of material characterization. Suchmodels can make predictions for points not covered by the initial DOE.The CFD simulation can be used to effectively relate model outputdirectly to defect formation and microstructural distributions.

The orthogonal parameter space defined by the DOE is used to generate amultitude of reduced volume build samples using PBF AM with varyinglaser or electron beam parameters and/or feedstock chemistries. Thereduced volume samples are a fraction of an intended build article andare configured to be amenable for analysis using a variety of mechanicalcharacterization techniques. Accordingly, the physical outputs of thesereduced volume build samples are mechanically characterized and analyzedto provide the optimal parameter set for a 3D article or a validationsample, which can provide an increased understanding of the parametersand their independent and confounding effects on defects andmicrostructure. Unlike a design that changes one factor at a time, whichis relatively inefficient, the DOE can be used to determine the relativesensitivities of the different parameters, e.g., hatch spacing, laserpower, velocity, layer thickness, recoating time, recoater speed, gasflow rate, gas concentration and/or the like, to provide an optimalparameter space. Prior PBF AM processes generally relied on manufacturerrecommendations for selection of processing parameters, which may or maynot exist and are not always optimal; relied on visual inspection, whichis subjective and has relatively low sensitivity; and/or relied onlaboratory scale characterization, which is typically time-consuming andexpensive as well as requiring complex data reduction.

Conventional techniques related to additive manufacturing processes forforming three-dimensional articles may or may not be described in detailherein. Moreover, the various tasks and process steps described hereincan be incorporated into a more comprehensive procedure or processhaving additional steps or functionality not described in detail herein.In particular, various steps in the additive manufacture ofthree-dimensional articles are well known and so, in the interest ofbrevity, many conventional steps will only be mentioned briefly hereinor will be omitted entirely without providing the well-known processdetails.

For the purposes of the description hereinafter, the terms “upper”,“lower”, “top”, “bottom”, “left,” and “right,” and derivatives thereofshall relate to the described structures, as they are oriented in thedrawing figures. The same numbers in the various figures can refer tothe same structural component or part thereof. Additionally, thearticles “a” and “an” preceding an element or component are intended tobe nonrestrictive regarding the number of instances (i.e. occurrences)of the element or component. Therefore, “a” or “an” should be read toinclude one or at least one, and the singular word form of the elementor component also includes the plural unless the number is obviouslymeant to be singular.

Spatially relative terms, e.g., “beneath,” “below,” “lower,” “above,”“upper,” and the like, can be used herein for ease of description todescribe one element or feature's relationship to another element(s) orfeature(s) as illustrated in the figures.

The following definitions and abbreviations are to be used for theinterpretation of the claims and the specification. As used herein, theterms “comprises,” “comprising,” “includes,” “including,” “has,”“having,” “contains” or “containing,” or any other variation thereof,are intended to cover a non-exclusive inclusion. For example, acomposition, a mixture, process, method, article, or apparatus thatcomprises a list of elements is not necessarily limited to only thoseelements but can include other elements not expressly listed or inherentto such composition, mixture, process, method, article, or apparatus.

As used herein, the term “about” modifying the quantity of aningredient, component, or reactant of the invention employed refers tovariation in the numerical quantity that can occur, for example, throughtypical measuring and liquid handling procedures used for makingconcentrates or solutions. Furthermore, variation can occur frominadvertent error in measuring procedures, differences in themanufacture, source, or purity of the ingredients employed to make thecompositions or carry out the methods, and the like.

It will also be understood that when an element, such as a layer,region, or substrate is referred to as being “on” or “over” anotherelement, it can be directly on the other element or intervening elementscan also be present. In contrast, when an element is referred to asbeing “directly on” or “directly over” another element, there are nointervening elements present, and the element is in contact with anotherelement.

Referring now to FIG. 1A-C, there is shown an exemplary rapiddevelopment process flow for PBF AM in accordance with the presentdisclosure, generally designated by reference numeral 100, to provide anoptimal parameter space for a particular 3D article build.

In block 110 as shown in FIG. 1A, a computer simulation process such asCFD simulation of the PBF AM process is utilized to rapidly scan theparameter space, e.g., determine an ideal energy density, and establisha baseline parameter space that predicts a desired melt pool shape andsolidification. The CFD simulation can provide different simulated meltpool shapes including porosity formation within the melt pool based onsimulated energy densities during the PBF AM process. An exemplary CFDmodeling software for optimizing process parameters is FLOW-3D AM, whichis commercially available from FLOW-3D.

As noted above, CFD simulation can be used to simulate and analyze thePBF AM process to provide simulations of melt pool dynamics and porosityformation for subsequent analysis and optimization of the processparameters. For example, macroscopic porosity (with pore sizes greaterthan the CFD modeling mesh size) can be extracted and characterizedagainst mechanical characterization data such as, for example, X-raycomputed tomography (XRCT) from an actual line track orthree-dimensional specimen. Metrics such as size and shape can bequantified and locally compared along a single track, including thestart and end of track, where defects often occur despite having optimallaser parameters. In addition, porosity smaller than the CFD modelingmesh size can be estimated using this technique by tracking evaporatedgas particles. These data can be compared with a suitable mechanicalcharacterization technique on the reduced volume build samples forvalidation. Once validated, the data can be used to generate a robustprocessing space.

The melt pool simulations can be performed using CFD layer by layer. Forexample, melt pool simulations can be made on the first melt layer of aselective laser melting (SLM) process and additional layers byspecifying the laser process parameters. This process can be repeatedseveral times to evaluate the fusing between consecutively solidifiedlayers and the temperature gradients within the build while alsomonitoring the formation of porosity or other defects. This informationis then used to predict the material density (or presence of porositydefects) and microstructure.

The CFD simulation can occur on an AM printer used for PBF, on a user'sdirectly connected personal computer (“PC”), on a local computerconnected by a local area network or using cloud computing. Theparameters generated from the CFD simulation can include, but are notlimited to, ambient conditions such as temperature, humidity andpressure; build speed; material conditions such as temperature andviscosity; layer thickness; and power profiles of the energy beam.

In block 120, also shown in FIG. 1A, a parameter set is selected fromthe CFD simulation and used to develop a robust and intelligent designof experiments (DOE) that utilizes a relatively large sample set. Thestatistics-based DOE allows multifactorial sampling of an orthogonalparameter space and elucidates the sensitivity of each parameter beingexamined, e.g., hatch spacing, laser power, velocity, layer thickness,and the like. In fact, an intelligent design paired with a large sampleset allows for separation of processing parameters in order to findrelationships and trends. In this manner, variability in process andbetween hardware can be readily and easily compared.

In block 130 as shown in FIG. 1B, samples are made in a PBF AM device inaccordance with the DOE to statistically sample the independentprocessing parameters about the simulated parameter set. The sample setcan be made with a reduced build volume to minimize cost, powder volume,and build time. Advantageously, the sample set can expeditiously bebuilt on the same substrate and subsequently removed from the substratefor mechanical characterization. In one or more embodiments, the PBF AMprocess can be configured to provide each sample with differentidentification for ease in subsequent analysis. For example, thedifferent samples can be consecutively numbered as shown in block 130.The reduced build volume is a fraction of the build volume for theintended 3D article to be built. Moreover, the reduced build volume cantake any form amenable to subsequent high throughput mechanicalcharacterization.

In block 140, as shown in FIG. 1C, the reduced volume build samples arecharacterized using high throughput characterization techniques such asX-ray computed tomography (XRCT) to down select the most desiredprocessing parameters. The particular mechanical characterizationtechnique is not intended to be limited. Exemplary mechanicalcharacterization techniques include, without limitation, optical andX-ray microscopy, Archimedes method, microhardness, tensile strength,and the like. The various mechanical characterization techniques can beused, for example, to characterize porosity such as by measuring surfaceroughness, tensile strength, hardness, dimensional accuracy, percentvolume fraction, size and shape deviation, type of defect, e.g., lack offusion or keyhole, defect distribution, i.e., edge defects compared toinfill defects, and the like. For example, the Archimedes method is aclassic approach for determining the density of a sample, usingρ=(Ma/Ma−Mw) ρW (1) where ρW is the density of water, Ma is the mass ofthe sample as measured in air, and Mw is the mass of the sample asmeasured in water. Once the density is measured, if the bulk density isalso known, then the sample's porosity can be calculated.

The mechanical characterization of the sample set generated from the DOEcan be used to predict the ideal parameter set to be used for the 3Darticle to be built. For example, total porosity can be determined for agiven parameter space defined by the DOE as a function of increasingenergy density and increasing exposure times, which can be mechanicallyanalyzed to determine an optimal parameter space for building the 3Darticle, which can have a significantly larger volume and a completelydifferent geometry. For example, mechanical characterization of a sampleset using high throughput XRCT can take the form of relatively smalldiameter cylinders conducive to substantially complete volume analysisby XRCT whereas for tensile strength mechanical characterization thesample set can take the form of non-standard reduced sized elongatedbars as generally defined in ASTM E8. The particular shape and form ofthe reduced volume sample set are not intended to be limited and aregenerally dimensioned to provide confident analysis by the mechanicalcharacterization technique.

With regard to XRCT, the reduced volume sized samples permit the use ofthis particular mechanical characterization technique for highthroughput material development. XRCT is a technique that obtains X-rayimages through a sample as it is rotated. The specimen is subjected toX-rays from many angles by rotating the specimen through approximately1,000 small angular increments between 0 and 360°. In one or moreembodiments conducive to high throughput processing, the cylinderdiameter is less than about 10 millimeters. In one or more embodiments,the cylinder diameter is less than about 7 millimeters and in still oneor more other embodiments, the cylinder diameter is less than about 5millimeters.

XRCT had previously only been used for qualification of a 3D articleand/or for failure analysis. However, because the reduced volume samplesare a fraction of the volume used for the intended 3D article build andthe geometries can be selected to be amenable for use in high throughputXRCT, XRCT can now be used for porosity characterization in materialdevelopment to rapidly identify defect characteristics, which can becorrelated back to the feedstock chemistry and/or input energy. Voxelresolution on the order of about 10 to about 20 micrometers can beobtained with cycle times of about 1 to 2 minutes. Block 140 pictoriallyillustrates sample 28 of an exemplary DOE showing the raw processed CTfor the cylinder volume (left), a processed defect analysis showing thedefects (center), and processed and computed internal meshed volume(right). As shown, the raw data was processed down to the defectsegmentation. Once the image is segmented, the defects can be exportedinto a near surface and volume using the meshed sample. Total porosityfor each of the different samples in the sample set is also graphicallyshown for the exemplary DOE.

FIG. 2 pictorially and graphically depicts an exemplary CFD simulationof an melt pool solidification in a laser powder bed fusion (L-PBF) AMprocess depicting the formation of a lack of fusion defect generallyresulting from a low energy density during AM processing (top left), anideal melt pool solidification having full density (center left), and akeyhole defect resulting from a high energy density during AM processing(bottom left), and the resulting laser power-velocity processing space(right) graphically generated from the simulation data. The parametereffects on energy density can be readily characterized and used tounderstand the laser-material intersections, and also can be used totake into account layer thickness, which can also affect the laserinteraction volume. As layer thickness increases, the volume of materialmelted is increased non-linearly, which can shift “defect-free”processing regions, in unexpected directions. The present disclosure canbe used to provide a processing space that avoids these shifts leadingto an optimal “ideal melt pool” processing space that can be utilized toform a desired 3D article.

FIG. 3 graphically illustrates an exemplary design of experiments (DOE)using JMP statistical software including adjusted desirability plots forminimal porosity for different parameters used to provide a certainenergy density. The different laser processing parameters included hatchspacing, point distance, exposure time, and power parameters in an L-PBFAM process. The interaction of these parameters is rather complex andthe optimized parameter set based on the desirability analysis of therelatively large orthogonal parameter space provided by the DOE and itseffect on total porosity as a function of energy density using theseparameters was independently confirmed as graphically shown. Totalporosity as a percentage of total volume was measured using highthroughput XRCT.

FIG. 4 illustrates a sample output of a DOE illustrating processparameter relationships on total porosity as a measured physical output.The graph on the left graphically illustrates porosity as a function ofenergy density by laser power and the graph on right illustratesporosity as a function of energy density by exposure time. Therelationship and trends observed on porosity by laser power and byexposure time can be readily determined and further understood toprovide an optimal parameter set that takes into account independentparameters as well as confounded parameters the sensitivities of each.

Referring now to FIGS. 5-8 , a rapid development process for optimizinga parameter space associated with additive manufacturing was generatedfor a steel alloy composition. FIG. 5 illustrates a DOE based on hatchspacing, point distance, velocity and power as parameters usingstatistical software that can provide an orthogonal output using aLatin-hypercube sampling method or the like. Exemplary commerciallyavailable statistical software that can be configured to provide varioussampling methods to produce an orthogonal output includes JMP software.The DOE was configured to provide a linear progression in power andexposure times for total porosity using an estimate of the optimalsettings. Other laser input parameters that can be included in a DOEinclude but are not limited to power, velocity, hatch, point distance,exposure time and machine parameters such as recoating time, layerthickness, or the like. As shown, each of the various rows of thereduced volume build samples were fabricated with constant exposuretimes and incremental increases in laser power whereas each of thevarious columns of the reduced build samples were fabricated withconstant power with incremental increase in exposure times.

In this DOE, 45 reduced volume cylindrical samples were built to betterunderstand and determine the ideal parameter space, e.g., hatch spacing,point distance, laser power, velocity, layer thickness, and the like, toprovide an optimal parameter space that provides the most ideal meltpool solidification for the given steel alloy feedstock chemistry. Theparticular steel alloy is not intended to be limited and is intended tobe exemplary of the process.

FIG. 6 graphically illustrates the resulting energy density for each ofthe samples. Sample ID 01 was fabricated with a laser power and exposuretime that provided the lowest energy density; sample ID 45 wasfabricated with a laser power and exposure time that provided thegreatest energy density; sample ID 23 was fabricated with a laser powerand exposure time in accordance with the manufacturers recommendedprocessing parameters; and sample ID 28 was fabricated with a laserpower and exposure time that provided the maximum density. Highthroughput XRCT was utilized to determine percent porosity associatedwith a specific parameter set. It should also be noted that ultimatetensile hardness and/or any other mechanical property including, but notlimited to, compression, fatigue and/or the like can be examined as aphysical output in a similar manner as may desired. Likewise, variousmicroscopy techniques can be used including, but not limited to,scanning electron microscopy, energy dispersive microscopy, x-raydiffraction microscopy, and like high throughput non-beamlinetechniques.

FIG. 7 graphically illustrates the total porosity as a percentage ofvolume (bar graph) and sample form (line graph) for each of the samplesin the sample set as measured using high throughput XRCT. The term“sample form” generally refers to the dimensional deviation from a“perfect” part or surface and is similar to surface roughnessmeasurement. The XRCT process was configured to provide a cycle time foreach sample of about 1 to 2 minutes with a voxel resolution of about 10to 20 microns. In XRCT, an x-ray is transmitted through a sample andmeasured on a target. The sample is rotated and frames are taken atpredetermined rotation intervals. This allows extraction ofthree-dimensional (3-D) defects in the sample based on stereographicprojections. The intensity of the beam and rotation interval can beoptimized to have a high signal to noise ratio. Trades in sample sizeand XRCT configurations are made to maximize detail while minimizingscan time.

As noted above, sample identification number 23 was the stock processingrecipe recommended by the manufacturer, which provided a total porosityof about 0.295% and a sample form of about 0.9 mm whereas sampleidentification number 28 represented the optimal process and a markedimprovement relative to the other samples, which provided the lowesttotal porosity of about 0.095% and a sample form of about 0.7 mm. Foradditional comparison, the samples built with the least energy density,sample identification number 01, had a total porosity of about 1.247%and a sample form of about 0.07 mm whereas the sample built with thegreatest energy density, sample identification number 45, had a totalporosity of about 0.360% and a surface form of about 0.14 mm.

FIG. 8 pictorially illustrates a sectional view of the porosity and a 3Drendering through the cylindrical volume of sample identificationnumbers 01 (minimal energy density), 23 (stock), 28 (optimal), and 45(maximum energy density) using high throughput XRCT. Lack of fusionporosity defects were evident in sample identification number 01 andkeyhole defects were evident in sample identification numbers 23, 28 and45.

Turning now to FIGS. 9-12 , the process in accordance with the presentdisclosure was applied to an aluminum alloy. The aluminum alloy was a5000 series aluminum alloy although the selection of a particularaluminum alloy is intended to be non-limiting and exemplary of theprocess.

FIG. 9 graphically illustrates an optimal processing space as a functionof exposure time and power based on a predicted optimal starting pointobtained by computational modeling. Lack of fusion defects weregenerally predicted at the lower exposure times whereas keyhole bounddefects were generally predicted with the higher power settings.

FIG. 10 graphically illustrates an updated model simulation subsequentto the addition of secondary information, i.e., training data, from thefirst iteration of the DOE about the predicted optimal starting point,which as shown can change the optimal processing space such that in someareas there is expansion of the processing space and in other areasthere is contraction. In this manner, the process can advantageouslyutilize machine learning to learn from the data and continually improvethe accuracy of the processing space over time. Machine learning models,such as those based on neural networks or other regression techniques,can be trained and fine-tuned on data collected as additional parts ofthe processing space are explored by fabricating and characterizing newsamples.

The DOE included 52 reduced volume cylindrical samples and tension barsfor different parameter sets shown in Table 1 below to better understandand determine the ideal parameter space, e.g., hatch spacing, pointdistance, laser power, velocity, and the like, to provide an optimalparameter space that provides the most ideal melt pool solidificationfor the 5086 aluminum alloy feedstock chemistry. Layer thickness wasconstant. The powdered feedstock chemistry generally included chromiumin an amount within a range of 0.05 to 0.25 weight percent (wt %), amaximum of 0.1 wt % of copper, a maximum of 0.5 wt % of iron, magnesiumwithin a range of 3.5 to 4.5 wt %, manganese within a range of 0.2 to0.7 wt %, a maximum of 0.4 wt % of silicon, a maximum of 0.15 wt % oftitanium, a maximum of 0.25 wt % zinc with the remainder aluminum.

The reduced volume cylinders of the modified 5086 aluminum alloy werefabricated using a Renishaw AM400 AM printer having a diameter of 6millimeters (mm) and a height of 10 mm, which were conducive to XRCTimaging of the entire cylindrical volume. These were manufacturedalongside reduced size tension bars with 2 mm gauge for rapid screeningof tension strength, yield strength, and ductility. Layer thickness was30 micrometers (μm).

Table 1 provides the laser parameters used in the DOE to generate aparticular energy density and the resulting measured physical propertiesfor each sample.

TABLE 1 Ultimate Exp. Pt Layer Scal Scal Tensile Power Time Dist. HatchThickness Energy Core Contour Scal Strength Yield Specimen (W) (μsec)(mm) (mm) (mm) Density Porosity Porosity Roughness (MPa) ElongationStrength Modulus 1 275 40 80 0.080 30.0 57.3 0.61 0.06 22.35 465.8 0.11427.4 66.4 2 300 60 80 0.080 30.0 93.8 0.08 0.16 27.46 454.5 0.10 428.066.9 3 275 30 60 0.080 30.0 57.3 0.18 0.04 20.58 467.9 0.12 427.7 67.1 4275 50 100 0.080 30.0 57.3 0.28 0.08 21.32 466.2 0.09 431.8 70.0 5 27534 60 0.080 30.0 65.5 0.14 0.10 20.92 466.2 0.09 436.5 66.1 6 275 46 800.080 30.0 65.5 0.30 0.10 20.73 471.2 0.10 435.5 70.2 7 275 57 100 0.08030.0 65.5 0.21 0.10 21.39 469.3 0.11 435.1 69.2 8 275 40 60 0.080 30.076.4 0.06 0.20 22.07 458.0 0.10 428.4 69.0 9 275 53 80 0.080 30.0 76.40.08 0.14 22.16 463.7 0.09 434.7 70.7 10 275 67 100 0.080 30.0 76.4 0.070.15 22.50 470.4 0.11 440.4 69.0 11 275 45 60 0.080 30.0 85.9 0.07 0.1921.92 461.8 0.11 431.2 68.6 12 275 64 80 0.080 30.0 91.7 0.08 0.21 23.43462.3 0.10 431.5 67.5 13 275 80 100 0.080 30.0 91.7 0.07 0.20 24.09457.5 0.12 426.4 70.5 14 275 40 80 0.060 30.0 76.4 0.43 0.08 22.49 469.60.10 437.0 65.1 15 275 40 80 0.070 30.0 65.5 0.40 0.05 21.85 473.5 0.12436.8 68.3 16 275 40 80 0.075 30.0 61.1 0.40 0.03 21.54 473.3 0.11 434.768.1 17 275 40 80 0.085 30.0 53.9 0.48 0.04 20.16 474.7 0.10 435.7 72.018 275 40 80 0.090 30.0 50.9 0.57 0.05 21.32 468.5 0.10 431.0 67.9 19275 40 80 0.100 30.0 45.8 0.76 0.08 20.63 462.5 0.08 424.5 64.4 20 19240 80 0.080 30.0 40.0 6.89 2.18 20.77 408.6 0.02 384.1 58.8 21 216 40 800.080 30.0 45.0 4.11 0.99 21.49 443.5 0.05 409.5 66.8 22 240 40 80 0.08030.0 50.0 1.76 0.27 19.86 459.5 0.09 419.3 67.5 23 264 40 80 0.080 30.055.0 0.64 0.07 20.98 466.8 0.08 432.6 70.9 24 288 40 80 0.080 30.0 60.00.24 0.09 21.16 469.0 0.09 437.3 67.4 25 312 40 80 0.080 30.0 65.0 0.120.24 21.46 462.8 0.09 433.5 70.4 26 336 40 80 0.080 30.0 70.0 0.14 0.5622.58 454.7 0.07 434.0 65.5 27 360 40 80 0.080 30.0 75.0 0.33 1.02 22.31440.4 0.06 419.6 65.0 28 384 40 80 0.080 30.0 80.0 0.72 1.44 23.66 434.50.06 414.8 65.3 29 396 40 80 0.080 30.0 82.5 0.72 1.33 24.44 424.8 0.05409.1 63.6 30 370 32 67 0.100 30.0 59.6 0.24 0.54 22.45 464.8 0.08 434.570.0 31 317 34 60 0.100 30.0 60.0 0.16 0.38 20.89 463.7 0.08 433.9 69.832 354 46 90 0.100 30.0 60.0 0.14 0.41 21.08 465.4 0.07 430.4 70.4 33169 80 75 0.100 30.0 60.0 0.57 0.10 19.85 464.1 0.08 434.6 68.0 34 24253 71 0.100 30.0 60.0 0.05 0.03 21.04 471.3 0.11 436.5 68.7 35 260 90 940.085 30.0 97.1 0.08 0.19 23.06 447.2 0.11 421.5 65.2 36 105 113 450.090 30.0 97.5 4.12 1.14 20.42 441.4 0.02 422.4 66.6 37 105 125 500.090 30.0 97.5 3.96 1.11 20.40 436.9 0.02 422.0 67.1 38 300 60 60 0.10030.0 100.0 0.20 0.43 26.14 399.9 0.05 379.4 60.7 39 243 83 84 0.080 30.0100.6 0.04 0.02 23.14 459.7 0.13 432.5 65.6 40 285 101 84 0.110 30.0103.9 0.10 0.16 27.86 429.7 0.10 397.6 62.8 41 314 66 82 0.080 30.0105.5 0.10 0.29 28.52 443.8 0.09 417.7 64.9 42 128 81 40 0.080 30.0108.2 2.46 0.53 22.60 462.7 0.06 441.6 70.2 43 281 56 95 0.050 30.0110.8 0.36 0.09 25.21 468.2 0.11 435.3 68.4 44 219 80 96 0.050 30.0121.9 0.55 0.06 25.43 473.5 0.13 447.0 70.8 45 132 132 40 0.050 30.0290.4 1.35 0.17 24.91 453.7 0.12 444.2 72.7 46 200 88 40 0.050 30.0294.3 0.41 0.02 30.47 472.5 0.05 462.4 72.1 47 87 172 40 0.040 30.0313.6 5.44 1.36 20.85 399.0 0.01 391.9 63.2 48 237 107 60 0.040 30.0351.4 0.11 0.17 33.95 441.1 0.11 425.6 68.4 49 157 71 95 0.065 30.0 60.33.35 0.70 20.85 443.3 0.02 418.8 65.6 50 182 41 63 0.065 30.0 60.3 1.130.15 21.05 463.6 0.09 429.2 67.4 51 218 45 84 0.065 30.0 60.3 2.18 0.3120.87 440.8 0.03 412.0 64.6 52 230 45 89 0.065 30.0 60.3 1.92 0.27 20.44450.4 0.07 414.7 65.3 Ultimate Tensile Strength (mPa); Yield Strength(Mpa); Modulus (Gpa); Elongation (mm/mm)

FIG. 11 graphically illustrates energy density for the differentaluminum alloy samples. The model simulation predicted the optimalpredicted sample to be associated with specimen 1, i.e., sample 1.However, as shown in FIG. 12 , the DOE about the parameter spaceassociated with sample 1 indicated that sample 34 yielded the optimalresults in terms of porosity and surface roughness, Sample 1 exhibited atotal porosity of 0.62% by volume (vol %) and a surface roughness of22.35 μm whereas Sample 34 exhibited a markedly lower total porosity of0.05 vol % and a surface roughness of 21.04 μm. It is important to notethat results for the porosity and surface roughness properties wereafter a single build. It can be expected that machine learning can beused, if needed, to further optimize and improve upon the porosity andsurface roughness properties after additional secondary information isobtained with an additional DOE and sample build about the parameterspace of Sample 34. In this case, machine learning models, such asneural networks or other regression-based methods, can be trained toapproximate the relationship between build input parameters (such aspower, hatch, speed) and material properties (such as porosity andsurface roughness). Moreover, the processing space about a particularoptimum can be determined to provide an end user with informationconcerning the stability of the process associated with a particularfeedstock chemistry and additive manufacturing device.

Turning now to FIG. 13 , there is graphically shown the effect offeedstock chemistry on total porosity as a function of energy densityfor a metal alloy composition with and without 5% by weight a dopantmaterial using a DOE parameter space defined by CFD simulation. Totalporosity was determined using high throughput XRCT. As shown, theaddition of the dopant material to the metal alloy powder feedcomposition shifted the build space resulting in a higher minimum energycompared to the behavior of additive manufactured metal alloycomposition without the dopant material. However, as shown, a lowerfloor on minimum porosity was also advantageously observed for the metalalloy composition with the dopant material compared to the behavior ofmetal alloy composition without the dopant material. From this design ofexperiment data, a robust processing parameter space based on parameterrelationships and trends can be selected to provide minimal totalporosity as well as minimal energy density requirements that providesthe desired amount of total porosity.

FIG. 14 illustrates an exemplary computer system 1000 for the rapiddevelopment additive manufacturing method of in accordance with one ormore embodiments of the present disclosure. The methods described hereincan be implemented in hardware, software (e.g., firmware), or acombination thereof. In one or more exemplary embodiments of the presentdisclosure, the methods including the computational fluid dynamicsimulation, design of experiments, and the AM apparatus and subsequentanalysis of one or more physical outputs described herein areimplemented in hardware as part of the microprocessor of a special orgeneral-purpose digital computer, such as a personal computer,workstation, minicomputer, or mainframe computer. The system 1000therefore may include general-purpose computer or mainframe 1001 capableof running multiple instances of an O/S simultaneously.

In one or more exemplary embodiments of the present disclosure, in termsof hardware architecture, the computer 1001 includes one or moreprocessors 1005, memory 1010 coupled to a memory controller 1015, andone or more input and/or output (I/O) devices 1040, 1045 (orperipherals) that are communicatively coupled via a local input/outputcontroller 1035. The input/output controller 1035 can be, for examplebut not limited to, one or more buses or other wired or wirelessconnections, as is known in the art. The input/output controller 1035may have additional elements, which are omitted for simplicity, such ascontrollers, buffers (caches), drivers, repeaters, and receivers, toenable communications. Further, the local interface may include address,control, and/or data connections to enable appropriate communicationsamong the aforementioned components. The input/output controller 1035may include a plurality of sub-channels configured to access the outputdevices 1040 and 1045. The sub-channels may include fiber-opticcommunications ports.

The processor 1005 is a hardware device for executing software includingCFD, DOE, AM processing software and/or physical output analyticalsoftware, particularly that stored in storage 1020, such as cachestorage, or memory 1010. The processor 1005 can be any custom made orcommercially available processor, a central processing unit (CPU), anauxiliary processor among several processors associated with thecomputer 1001, a semiconductor based microprocessor (in the form of amicrochip or chip set), a macroprocessor, or generally any device forexecuting instructions.

The memory 1010 can include any one or combination of volatile memoryelements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM,etc.)) and nonvolatile memory elements (e.g., ROM, erasable programmableread only memory (EPROM), electronically erasable programmable read onlymemory (EEPROM), programmable read only memory (PROM), tape, compactdisc read only memory (CD-ROM), disk, diskette, cartridge, cassette orthe like, etc.). Moreover, the memory 1010 may incorporate electronic,magnetic, optical, and/or other types of storage media. Note that thememory 1010 can have a distributed architecture, where variouscomponents are situated remote from one another, but can be accessed bythe processor 1005.

The instructions in memory 1010 may include one or more separateprograms, each of which comprises an ordered listing of executableinstructions for implementing logical functions. In the example of FIG.14 , the instructions in the memory 1010 provides a suitable operatingsystem (OS) 1011. The operating system 1011 essentially controls theexecution of other computer programs and provides scheduling,input-output control, file and data management, memory management, andcommunication control and related services. In one or more embodiments,the instructions, e.g., code, can be stored on a non-transitory computerreadable medium, wherein the term “non-transitory” generally refers tocomputer-readable media that stores data for short periods or in thepresence of power such as a memory device or Random Access Memory (RAM).

In accordance with one or more embodiments of the present invention, thememory 1010 may include multiple logical partitions (LPARs) each runningan instance of an operating system. The LPARs may be managed by ahypervisor, which may be a program stored in memory 1010 and executed bythe processor 1005.

In one or more exemplary embodiments of the present disclosure, aconventional keyboard 1050 and mouse 1055 can be coupled to theinput/output controller 1035. Other output devices such as the I/Odevices 1040, 1045 may include input devices, for example but notlimited to a printer, a scanner, microphone, and the like. Finally, theI/O devices 1040, 1045 may further include devices that communicate bothinputs and outputs, for instance but not limited to, a network interfacecard (NIC) or modulator/demodulator (for accessing other files, devices,systems, or a network), a radio frequency (RF) or other transceiver, atelephonic interface, a bridge, a router, and the like. The system 1000can further include a display controller 1025 coupled to a display 1030.

In one or more exemplary embodiments of the present disclosure, thesystem 1000 can further include a network interface 1060 for coupling toa network 1065. The network 1065 can be an IP-based network forcommunication between the computer 1001 and any external server, clientand the like via a broadband connection. The network 1065 transmits andreceives data between the computer 1001 and external systems, e.g., anAM printer. In an exemplary embodiment, network 1065 can be a managed IPnetwork administered by a service provider. The network 1065 may beimplemented in a wireless fashion, e.g., using wireless protocols andtechnologies, such as WiFi, WiMax, etc. The network 1065 can also be apacket-switched network such as a local area network, wide area network,metropolitan area network, Internet network, or other similar type ofnetwork environment. The network 1065 may be a fixed wireless network, awireless local area network (LAN), a wireless wide area network (WAN) apersonal area network (PAN), a virtual private network (VPN), intranetor other suitable network system and includes equipment for receivingand transmitting signals.

If the computer 1001 is a PC, workstation, intelligent device or thelike, the instructions in the memory ZZ10 may further include a basicinput output system (BIOS) (omitted for simplicity). The BIOS is a setof essential software routines that initialize and test hardware atstartup, start the OS 1011, and support the transfer of data among thehardware devices. The BIOS is stored in ROM so that the BIOS can beexecuted when the computer 1001 is activated.

When the computer 1001 is in operation, the processor 1005 is configuredto execute instructions stored within the memory 1010, to communicatedata to and from the memory 1010, and to generally control operations ofthe computer 1001 pursuant to the instructions.

The computer-readable medium can be a manufactured product, such as harddrive in a computer system or an optical disc sold through retailchannels, or an embedded system. The computer-readable medium can beacquired separately and later encoded with the one or more modules ofcomputer program instructions, such as by delivery of the one or moremodules of computer program instructions over a wired or wirelessnetwork. The computer-readable medium can be a machine-readable storagedevice, a machine-readable storage substrate, a memory device, or acombination of one or more of them.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, declarative orprocedural languages, and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, or other unitsuitable for use in a computing environment. A computer program does notnecessarily correspond to a file in a file system. A program can bestored in a portion of a file that holds other programs or data (e.g.,one or more scripts stored in a markup language document), in a singlefile dedicated to the program in question, or in multiple coordinatedfiles (e.g., files that store one or more modules, sub-programs, orportions of code). A computer program can be deployed to be executed onone computer or on multiple computers that are located at one site ordistributed across multiple sites and interconnected by a communicationnetwork.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application-specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read-only memory ora random-access memory or both. The essential elements of a computer area processor for performing instructions and one or more memory devicesfor storing instructions and data. Generally, a computer will alsoinclude, or be operatively coupled to receive data from or transfer datato, or both, one or more mass storage devices for storing data, e.g,magnetic, magneto-optical disks, or optical disks. However, a computerneed not have such devices. Moreover, a computer can be embedded inanother device, e.g., a mobile telephone, a personal digital assistant(PDA), a mobile audio or video player, a game console, a GlobalPositioning System (GPS) receiver, or a portable storage device (e.g., auniversal serial bus (USB) flash drive), to name just a few. Devicessuitable for storing computer program instructions and data include allforms of non-volatile memory, media and memory devices, including by wayof example semiconductor memory devices, e.g., EPROM (ErasableProgrammable Read-Only Memory), EEPROM (Electrically ErasableProgrammable Read-Only Memory), and flash memory devices; magneticdisks, e.g., internal hard disks or removable disks; magneto-opticaldisks; and CD-ROM and DVD-ROM disks. The processor and the memory can besupplemented by, or incorporated in, special purpose logic circuitry.

To provide for interaction with a user, embodiments of the subjectmatter described in this specification can be implemented on a computerhaving a display device, e.g., a CRT (cathode ray tube) or LCD (liquidcrystal display) monitor, for displaying information to the user and akeyboard and a pointing device, e.g., a mouse or a trackball, by whichthe user can provide input to the computer. Other kinds of devices canbe used to provide for interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback, e.g.,visual feedback, auditory feedback, or tactile feedback; and input fromthe user can be received in any form, including acoustic, speech, ortactile input.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other,Embodiments of the subject matter described in this specification can beimplemented in a computing system that includes a back-end component,e.g., as a data server, or that includes a middleware component, e.g.,an application server, or that includes a front-end component, e.g., aclient computer having a graphical user interface or a Web browserthrough which a user can interact with an implementation of the subjectmatter described is this specification, or any combination of one ormore such back-end, middleware, or front-end components. The componentsof the system can be interconnected by any form or medium of digitaldata communication, e.g., a communication network.

While this specification contains many implementation details, theseshould not be construed as limitations on the scope of the invention orof what may be claimed, but rather as descriptions of features specificto particular embodiments of the invention. Certain features that aredescribed in this specification in the context of separate embodimentscan also be implemented in combination in a single embodiment.Conversely, various features that are described in the context of asingle embodiment can also be implemented in multiple embodimentsseparately or in any suitable sub-combination. Moreover, althoughfeatures may be described above as acting in certain combinations andeven initially claimed as such, one or more features from a claimedcombination can in some cases be excised from the combination, and theclaimed combination may be directed to a sub-combination or variation ofa sub-combination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems cangenerally be integrated together in a single software product orpackaged into multiple software products.

This written description uses examples to disclose the invention,including the best mode, and also to enable any person skilled in theart to make and use the invention. The patentable scope of the inventionis defined by the claims, and may include other examples that occur tothose skilled in the art. Such other examples are intended to be withinthe scope of the claims if they have structural elements that do notdiffer from the literal language of the claims, or if they includeequivalent structural elements with insubstantial differences from theliteral languages of the claims.

What is claimed is:
 1. A system comprising: at least one computercommunicatively coupled to a three-dimensional additive manufacturingprinter; and a mechanical characterization device configured to measureone or more physical outputs associated with additive manufacturedreduced volume samples, wherein the reduced volume samples are afraction of an intended build article; wherein the at least one computeris configured to provide a computational fluid dynamic simulation of apowder bed fusion additive manufacturing process to simulate melt pooldynamics and provide a simulated optimal parameter set, wherein the atleast one computer is configured to provide a first statistical designof experiments based on the simulated optimal parameter set to generatea multi-factorial parameter space encompassing the simulated optimalparameter set and provide instructions to the three-dimensional additivemanufacturing printer to build the reduced volume samples according tothe multi-factorial parameter space to determine a first optimal buildparameter set, and wherein the at least one computer is optionallyconfigured to provide at least one additional statistical design ofexperiments based on the first optimal build parameter set to generateat least one additional multi-factorial parameter space encompassing thefirst optimal build parameter set and provide instructions to thethree-dimensional additive manufacturing printer to build the reducedvolume samples according to the at least one additional multi-factorialparameter space to determine at least one additional optimal buildparameter set for the first optimal build parameter set.
 2. The systemof claim 1, wherein measuring the one or more physical outputs comprisesoptical and X-ray microscopy, Archimedes method, microhardness, tensilestrength, or combinations thereof.
 3. The system of claim 1, wherein themeasuring the one or more physical outputs comprises using x-raycomputed tomography (XRCT) for volume analysis of the reduced volumesamples.
 4. The system of claim 3, wherein the XRCT is configured toprovide a voxel resolution of about 1 to about 200 micrometers and acycle time of about 1 to about 2 minutes for each of the reduced volumesamples.
 5. The system of claim 1, wherein the powder bed fusionadditive manufacturing process is a laser powder bed fusionmanufacturing process or an electron beam powder bed fusionmanufacturing process or a directed energy deposition process.
 6. Thesystem of claim 1, wherein the at least one computer is furtherconfigured to determine the first optimal build parameter set from thefirst optimal build parameter space and execute build instructions tothe additive manufacturing device based on the first optimal buildparameter set.
 7. The system of claim 1, wherein the one or morephysical outputs comprise percent porosity, tensile strength, surfaceroughness, elongation, or combinations thereof.
 8. The system of claim1, further comprising analyzing the one or more physical outputsassociated with each of the reduced volume samples; correlating defectmorphology to one or more parameters used within the parameter space;and determining the first or the at least one additional optimized buildparameter set based on selected ones of the one or more physicaloutputs.
 9. The system of claim 8, further comprising instructing thethree-dimensional additive manufacturing printer to produce a buildarticle or a validation sample using the first optimized build parameterset or the at least one additional optimized build parameter set on alayer-by-layer basis.
 10. The system of claim 8, wherein analyzing theone or more physical outputs associated with each of the reduced volumesamples comprises measuring total porosity as a function of energydensity; and determining the first or the at least one additionaloptimized build parameter set from the total porosity.
 11. The system ofclaim 1, wherein the selected parameter space comprises parametersselected from the group consisting of layer thickness, hatch spacing,exposure time, velocity, power, recoating time, recoater speed, gas flowrate, gas concentration and combinations thereof.